Spatial targeting evaluation of energy and environmental performance of waste-to-energy processing

doi:10.1007/s11705-018-1772-1

Front. Chem. Sci. Eng.

2018, Vol. 12

Issue (4) : 731-744 https://doi.org/10.1007/s11705-018-1772-1

RESEARCH ARTICLE

Spatial targeting evaluation of energy and environmental performance of waste-to-energy processing

Petar S. Varbanov¹(

), Timothy G. Walmsley¹, Yee V. Fan¹, Jiří J. Klemeš¹, Simon J. Perry²

¹. Sustainable Process Integration Laboratory, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology, 616 69 Brno, Czech Republic
². Centre for Process Integration, School of Chemical Engineering and Analytical Science, The University of Manchester, Manchester M1 3AL, UK

Download: PDF(550 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Waste-to-energy supply chains are important potential contributors to minimising the environmental impacts of municipal solid waste by reducing the amounts of waste sent to landfill, as well as the fossil fuel consumption and environmental footprints. Accounting for the spatial and transport properties of the waste-to-energy supply chains is crucial for understanding the problem and improving the supply chain designs. The most significant challenge is the distributed nature of the waste generation and the household energy demands. The current work proposes concepts and a procedure for targeting the size of the municipal solid waste collection zone as the first step in the waste-to-energy supply chains synthesis. The formulated concepts and the provided case study reveal trends of reducing the net greenhouse gas savings and energy recovery by increasing the collection zone size. Population density has a positive correlation with the greenhouse gas saving and energy recovery performance. For smaller zone size the energy recovery from waste approaches and in some cases may surpass the energy spent on waste transportation. The energy recovery and greenhouse gas savings remain significant even for collection zones as large as 200 km². The obtained trends are discussed and key directions for future work are proposed.

Keywords waste-to-energy supply chain optimisation GHG savings energy recovery ratio

Corresponding Author(s): Petar S. Varbanov

Online First Date: 21 December 2018 Issue Date: 03 January 2019

Cite this article:

Petar S. Varbanov,Timothy G. Walmsley,Yee V. Fan, et al. Spatial targeting evaluation of energy and environmental performance of waste-to-energy processing[J]. Front. Chem. Sci. Eng., 2018, 12(4): 731-744.

URL:

https://academic.hep.com.cn/fcse/EN/10.1007/s11705-018-1772-1
https://academic.hep.com.cn/fcse/EN/Y2018/V12/I4/731

Fig.1 WtE processing workflow (amended from [²⁰])

Fig.2 The main modelling concept: approximating an actual collection zone (after Chalkias and Lasaridi [²³]), with an equivalent collection zone (amended from [²⁰])

Fig.3 A visual superstructure of the WtE problem in P-graph studio (amended from [²⁰])

Tab.1 CHP gas engine options—“Jenbacher type 3”

Tab.2 Evaluation plan and results*

Fig.4 Relative GHG saving trend

Fig.5 ERR trend

$c^hand$ / (€?t^?1)	Waste handling fee
$m^w,gen$ /(t?ihnabitant^?1·y^?1)	Specific waste generation per individual
$c hand$ /(€?y^?1)	Waste handling cost item
$c trans$ /(€?y^?1)	Waste transportation cost item
$E CHP$ /(GJ?y^?1)	The sum of the heat and power flows generated by the WtE processes
$N S GHG$ /(t?y^?1)	Net savings of GHG
$R S GHG$ /%	Relative GHG Saving
$d ave$ /km	Average transportation distance
$e t$ /(t_fuel?t_waste^?1·km^?1)	Average truck specific energy consumption
$p f$ /(€?t_fuel^?1)	Price of the transport fuel
A /km²	Area of the ECZ
COP /1	The coefficient of performance (heat pumps)
ERR /1	Energy recovery ratio
FT /(GJ?y^?1)	Fuel energy for transportation
i, m, n, j	Indices for facilities and operating units in the layer model (Fig. 1)
m_tot /(t?y^?1)	Total waste mass flow
N_i, N_m, N_n, N_j	Numbers of facilities and operating units within each of the layers in Fig. 1
NO_x /ppm	Oxides of nitrogen (content)
P_den, PD/ihnabitants per km²	Population density
r /km	The radius of the ECZ
TC / (€?y^?1)	Overall waste transportation cost
β /1	Additional transport distance performance coefficient

1	TNagy, P Mizsey. Model verification and analysis of the CO2-MEA absorber–desorber system. International Journal of Greenhouse Gas Control, 2015, 39: 236–244 https://doi.org/10.1016/j.ijggc.2015.05.017
2	Y VFan, P S Varbanov, J J Klemeš, A Nemet. Process efficiency optimisation and integration for cleaner production. Journal of Cleaner Production, 2018, 174: 177–183 https://doi.org/10.1016/j.jclepro.2017.10.325
3	ZFodor, J J Klemeš. Waste as alternative fuel—Minimising emissions and effluents by advanced design. Process Safety and Environmental Protection, 2012, 90(3): 263–284 https://doi.org/10.1016/j.psep.2011.09.004
4	IBoumanchar, Y Chhiti, F E M HAlaoui, AEl Ouinani, ASahibed-Dine, FBentiss, CJama, M Bensitel. Effect of materials mixture on the higher heating value: Case of biomass, biochar and municipal solid waste. Waste Management (New York, N.Y.), 2017, 61: 78–86 https://doi.org/10.1016/j.wasman.2016.11.012
5	SDas, B K Bhattacharyya. Optimization of municipal solid waste collection and transportation routes. Waste Management (New York, N.Y.), 2015, 43: 9–18 https://doi.org/10.1016/j.wasman.2015.06.033
6	YZhang, G H Huang, L He. A multi-echelon supply chain model for municipal solid waste management system. Waste Management (New York, N.Y.), 2014, 34(2): 553–561 https://doi.org/10.1016/j.wasman.2013.10.002
7	FCucchiella, I D’Adamo, MGastaldi. Sustainable management of waste-to-energy facilities. Renewable & Sustainable Energy Reviews, 2014, 33: 719–728 https://doi.org/10.1016/j.rser.2014.02.015
8	ZXu, A Elomri, SPokharel, QZhang, X GMing, WLiu. Global reverse supply chain design for solid waste recycling under uncertainties and carbon emission constraint. Waste Management (New York, N.Y.), 2017, 64: 358–370 https://doi.org/10.1016/j.wasman.2017.02.024
9	DLongden, J Brammer, LBastin, NCooper. Distributed or centralised energy-from-waste policy? Implications of technology and scale at municipal level. Energy Policy, 2007, 35(4): 2622–2634 https://doi.org/10.1016/j.enpol.2006.09.013
10	MGandy. Political conflict over waste-to-energy schemes. Land Use Policy, 1995, 12(1): 29–36 https://doi.org/10.1016/0264-8377(95)90072-A
11	F J CPirotta, E CFerreira, C ABernardo. Energy recovery and impact on land use of Maltese municipal solid waste incineration. Energy, 2013, 49: 1–11 https://doi.org/10.1016/j.energy.2012.10.049
12	GMavrotas, N Gakis, SSkoulaxinou, VKatsouros, EGeorgopoulou. Municipal solid waste management and energy production: Consideration of external cost through multi-objective optimization and its effect on waste-to-energy solutions. Renewable & Sustainable Energy Reviews, 2015, 51: 1205–1222 https://doi.org/10.1016/j.rser.2015.07.029
13	PStasta, J Boran, LBébar, PStehlík, JOral. Thermal processing of sewage sludge. Applied Thermal Engineering, 2006, 26(13): 1420–1426 https://doi.org/10.1016/j.applthermaleng.2005.05.030
14	BKilkovsky, P Stehlík, ZJegla, L LTovazhnyansky, OArsenyeva, P OKapustenko. Heat exchangers for energy recovery in waste and biomass to energy technologies—I. Energy recovery from flue gas. Applied Thermal Engineering, 2015, 64(1-2): 213–223 https://doi.org/10.1016/j.applthermaleng.2013.11.041
15	J DMurphy, E McKeogh. Technical, economic and environmental analysis of energy production from municipal solid waste. Renewable Energy, 2004, 29(7): 1043–1057 https://doi.org/10.1016/j.renene.2003.12.002
16	K MNazmul Islam. Greenhouse gas footprint and the carbon flow associated with different solid waste management strategy for urban metabolism in Bangladesh. Science of the Total Environment, 2017, 580: 755–769 https://doi.org/10.1016/j.scitotenv.2016.12.022
17	GTavares, Z Zsigraiová, VSemiao. Multi-criteria GIS-based siting of an incineration plant for municipal solid waste. Waste Management (New York, N.Y.), 2011, 31(9-10): 1960–1972 https://doi.org/10.1016/j.wasman.2011.04.013
18	FMurphy, A Sosa, KMcDonnell, GDevlin. Life cycle assessment of biomass-to-energy systems in Ireland modelled with biomass supply chain optimisation based on greenhouse gas emission reduction. Energy, 2016, 109: 1040–1055 https://doi.org/10.1016/j.energy.2016.04.125
19	RŠomplák, VNevrlý, VSmejkalová, MPavlas, JKůdela. Verification of information in large databases by mathematical programming in waste management. Chemical Engineering Transactions, 2017, 61: 985–990
20	T GWalmsley, P S Varbanov, J J Klemeš. Networks for utilising the organic and dry fractions of municipal waste: P-graph approach. Chemical Engineering Transactions, 2017, 61: 1357–1362
21	MNelles, J Grünes, GMorscheck. Waste management in Germany—Development to a sustainable circular economy? Procedia Environmental Sciences, 2016, 35: 6–14 https://doi.org/10.1016/j.proenv.2016.07.001
22	RDekker, M Fleischmann, KInderfurth, LNWassenhove. Reverse Logistics: Quantitative Models for Closed-Loop Supply Chains. Springer Science & Business Media, 2013
23	CChalkias, K Lasaridi. Benefits from GIS Based Modelling for Municipal Solid Waste Management. In: Kumar S, ed. Integrated Waste Management—Vol I. London: InTech, 2011, DOI 10.5772/17087
24	Council Directive 1999/31/EC of 26 April 1999 on the landfill of waste, Official Journal L 182, 16/07/1999, 0001–0019.
25	WPeng, A Pivato. Sustainable management of digestate from the organic fraction of municipal solid waste and food waste under the concepts of back to earth alternatives and circular economy. Waste and Biomass Valorization, 2017, https://doi.org/10.1007/s12649-017-0071-2
26	FFriedler, J B Varga, E Fehér, L TFan. Combinatorially accelerated branch-and-bound method for solving the MIP model of process network synthesis. In: Floudas C A, Pardalos P M, eds. State of the Art in Global Optimization. Massachusetts, USA: Kluwer Academic Publishers, 1996, 609–626
27	ZKravanja. Challenges in sustainable integrated process synthesis and the capabilities of a MINLP process synthesizer MipSyn. Computers & Chemical Engineering, 2010, 34(11): 1831–1848 https://doi.org/10.1016/j.compchemeng.2010.04.017
28	LBranchini. Municipal waste overview In: Waste-to-Energy, Chapter 2. Switzerland: Springer International Publishing, 2015, 7–17
29	P SVarbanov, H L Lam, F Friedler, J JKlemeš. Energy generation and carbon footprint of waste to energy. Centralised vs. distributed processing. Computer-Aided Chemical Engineering, 2012, 31: 1402–1406 https://doi.org/10.1016/B978-0-444-59506-5.50111-5
30	MBerglund, P Börjesson. Assessment of energy performance in the life-cycle of biogas production. Biomass and Bioenergy, 2006, 30(3): 254–266 https://doi.org/10.1016/j.biombioe.2005.11.011
31	M AJohnston. Managing odors at anaerobic digestion plants. BioCycle, 2017, 58(3): 39
32	S TTan, C T Lee, H Hashim, W SHo, J SLim. Optimal process network for municipal solid waste management in Iskandar Malaysia. Journal of Cleaner Production, 2014, 71: 48–58 https://doi.org/10.1016/j.jclepro.2013.12.005
33	MPavlas, R Šomplák, VSmejkalová, VNevrlý, LSzásziová, JKůdela, PPopela. Spatially distributed production data for supply chain models—Forecasting with hazardous waste. Journal of Cleaner Production, 2017, 161: 1317–1328 https://doi.org/10.1016/j.jclepro.2017.06.107
34	VNevrlý, R Šomplák, JGregor, MPavlas, JKlemeš. Impact on the Population from the Transportation of Waste based on Emission Models. In: Proceedings of SDEWES 2017—12th Conference on Sustainable Development of Energy, Water and Environmental Systems. Zagreb: Faculty of Mechanical Engineering and Naval Architecture, 2017, 1‒14

[1]	Tomáš Ferdan, Martin Pavlas, Vlastimír Nevrlý, Radovan Šomplák, Petr Stehlík. Greenhouse gas emissions from thermal treatment of non-recyclable municipal waste[J]. Front. Chem. Sci. Eng., 2018, 12(4): 815-831.

Viewed

Full text

Abstract

Cited

Shared

Discussed